Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future markets' prediction factoring in the sentiment of investors and algorithmic traders is an exciting area for research with deep learning techniques emerging to understand the market and its future direction. The paper develops two FINBERT deep neural network models pre-trained on the financial phrase dataset, the first one to extract sentiment from the NSE market news. The second model is adopted to predict the stock market movement of NSE with the above sentiment, historical stock prices, return on investment, and risk as predictors. The accuracy is compared with RNN and LSTM and baseline machine learning classifiers like naïve bayes and support vector machine (SVM). The accuracy of the FINBERT model is found to out-perform the deep learning algorithms and above baseline machine learning classifiers thus justifying the importance of the FINBERT model in stock market prediction.

Big data has emerged as an important resource for generating wealth in society along with capital and labour, as data analytics generates valuable information and provides critical insights to gain competitive advantage. In Investment management, access to information is vital. As analytics causes information asymmetry among those who use it and others, it has become a key result area in the domain.IM involves multi-criteria decision-making necessitating managers to acquire core capability in analytics. The field of IM is passing through rapid changes, with varying customer preferences, advancing technologies, diminishing margins, acute competition, in the midst of increase in compliances. Cock-pit monitoring and goal-based portfolio preferences by some large customers have complicated IM. The paper explores the rationale for implementing big data analytics and identifies evolving tools and technologies that are applicable in the domain. The paper also highlights few emerging areas of research in the field using both bibliometric analysis and systemic literature review techniques.

Today, the advent of social media has provided a platform for expressing opinions regarding legislation and public schemes. One such burning legislation introduced in India is the Citizenship Amendment Act (CAA) and its impact on the National Citizenship Register (NRC) and, subsequently, on the National Population Register (NPR). This study examines and determines the opinions expressed on social media regarding the act through a Twitter analysis approach that extracts nearly 18,000 tweets during 10 days of introducing the scheme. The analysis revealed that the opinion was neutral but tended to a more negative reaction. Consequently, recommendations on improving public perception about the scheme by suitable for interpreting the Act to the public are provided in the paper.

The paper aims to examine the factors that influence employee attrition rate using the employee records dataset from kaggle.com. It also aims to establish the predictive power of Deep Learning for employee churn prediction over ensemble machine learning techniques like Random Forest and Gradient Boosting on real-time employee data from a mid-sized Fast-Moving Consumer Goods (FMCG) company. The results are further validated through a regression model and also by a multi-criteria Fuzzy Analytical Hierarchy Process (AHP) model which takes into account the relative variable importance and computes weights. The empirical results of the machine learning models indicate that Deep Neural Networks (91.2% accuracy) are a better predictor of churn than Random Forest and Gradient Boosting Algorithm (82.3% and 85.2% respectively). These findings provide useful insights for human resource (HR) managers in an organizational workplace context. The model when recalibrated by the human resource team of organizations helps in better incentivization and employee retention.

We are passing through information age with lightning communication speed. Information asset storage in Cloud and retrieval in the net has become the new invisible corporate voyage into the information space. Information Assets are a valuable source of Knowledge for both Information asset creator as well as the user. These are fluid assets that change overtime based on several internal and environmental factors! This paper seeks to address these aspects of Change that impacts such “fluid” Information Assets and the need to raise up to the changing expectations of the millions of users by satisfying the ever growing information hungry businesses. Providing unreliable information and sub-optimal analytical tools can destroy the user in the first instance and can lead to self-destruction as Information asset provider will find no takers in the long run. In this context this assorted information on Change management is chosen carefully and it is hoped, will benefit the reader who may be a technical expert in his field.